1102 Data acquisition and reconstruction of undersampled radial MR myocardial perfusion

Adluru, Ganesh; DiBella, Edward; McGann, Chris

doi:10.1186/1532-429x-10-s1-a227

Cited by 4 publications

(16 citation statements)

References 3 publications

(4 reference statements)

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“…Figures 1c and 2c show vastly improved quality over Figures 1b and 2b in terms of removing the artifacts. For comparison we show the images obtained from R=2 data using the 1 norm/TV approach [7][8][9]. Iterative gradient descent was used for TV reconstruction as described in [9].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…This method fits in the mathematical framework of compressed sensing, which suggests exact reconstructions of a sparse signal from incomplete Fourier data can be possible by minimizing its 1 norm [5,6]. The method was applied to accelerating a number of dynamic and static MR imaging techniques [7][8][9]. However a limitation of the TV approach is that fine structures in images can be lost or have poor contrast [8].…”

Section: Introductionmentioning

confidence: 99%

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Improving Undersampled MRI Reconstruction Using Non-local Means

Adluru¹,

Taşdizen

Whitaker³

et al. 2010

2010 20th International Conference on Pattern Recognition

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Section: Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Undersampled MRI Reconstruction Using Non-local Means

Adluru¹,

Taşdizen

Whitaker³

et al. 2010

2010 20th International Conference on Pattern Recognition

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“…norm gradient constraint (28). In contrast to TV, the difference between each pixel and a larger neighborhood of pixels is minimized with a certain weighting in NLM.…”

Section: ½5mentioning

confidence: 99%

“…[5] but using the total variation constraints on the image estimate. A gradient descent approach was used for the minimization process (28). We note that when the data D are fully sampled according to the Nyquist limit, this is equivalent to the original TV application for denoising.…”

Section: Total Variation Constrained Reconstructionmentioning

confidence: 99%

“…Recently, an inverse problem based total variation (TV) filtering has been extended to remove undersampling artifacts when reconstructing both static and dynamic MRI data (26)(27)(28). In the original application, the filter was applied to denoise images by iteratively minimizing a cost function consisting of an image fidelity term and an L 1 norm penalty term on image gradients (29).…”

mentioning

confidence: 99%

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Reconstruction of 3D dynamic contrast‐enhanced magnetic resonance imaging using nonlocal means

Adluru

Taşdizen

Schabel

et al. 2010

Magnetic Resonance Imaging

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Purpose: To develop and test a nonlocal means-based reconstruction algorithm for undersampled 3D dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of tumors. Materials and Methods:We propose a reconstruction technique that is based on the recently proposed nonlocal means (NLM) filter which can relax trade-offs in spatial and temporal resolutions in dynamic imaging. Unlike the original application of NLM for image denoising, the MR reconstruction framework here can offer high-quality images from undersampled k-space data. The method is based on enforcing similarity constraints in terms of neighborhoods of pixels rather than individual pixels. The method was applied on undersampled 3D DCE imaging of breast and brain tumor datasets and the results were compared to sliding window reconstructions and to a compressed sensing method using total variation constraints on the images.Results: Undersampling factors of up to five were obtained with the proposed approach while preserving the spatial and temporal characteristics. The NLM reconstruction method offered improved performance over the sliding window and the total variation constrained reconstruction techniques. Conclusion:The reconstruction framework here can give high-quality images from undersampled DCE MRI data and has the potential to improve the quality of DCE tumor imaging.

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An extended Bayesian-FBP algorithm

Zeng

Divkovic

2015

2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

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Recently we developed a Bayesian-FBP (Filtered Backprojection) algorithm for CT image reconstruction. This algorithm is also referred to as the FBP-MAP (FBP Maximum a Posteriori) algorithm. This non-iterative Bayesian algorithm has been applied to real-time MRI, in which the k-space is under-sampled. This current paper investigates the possibility to extend this Bayesian-FBP algorithm by introducing more controlling parameters. Thus, our original Bayesian-FBP algorithm became a special case of the extended Bayesian-FBP algorithm. A cardiac patient data set is used in this paper to evaluate the extended Bayesian-FBP algorithm, and the result from a well-establish iterative algorithm with L 1 -norms is used as the gold standard. If the parameters are selected properly, the extended Bayesian-FBP algorithm can outperform the original Bayesian-FBP algorithm.

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1102 Data acquisition and reconstruction of undersampled radial MR myocardial perfusion

Cited by 4 publications

References 3 publications

Improving Undersampled MRI Reconstruction Using Non-local Means

Improving Undersampled MRI Reconstruction Using Non-local Means

Reconstruction of 3D dynamic contrast‐enhanced magnetic resonance imaging using nonlocal means

An extended Bayesian-FBP algorithm

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